PROJECT ABSTRACT The goal of this Phase II SBIR project is to develop a method for creating algorithmically generated control groups ? virtual controls. These will be composed of synthetic cases that have two characteristics: (1) pretest similarity to whatever prevention treatment group may present itself and (2) patterns of change over time that closely mimic the normal course of alcohol and drug use development. The field of alcohol and drug prevention is one that has significantly matured and has rich data resources that can be employed to this end. Notably, numerous school and community efficacy studies have employed control groups in randomized control trials. Additional etiological and epidemiological studies have collected similar data. Given the large amount of data available for analysis, it is now possible to model the onset of alcohol and drug use among adolescents. We propose to gather data from previous studies and develop statistical models that can be used to predict the onset of alcohol and drug use. An algorithm will be developed that will create integrative control cases to match to a treatment group?s demographics and pretest mediating variable scores and then estimate future drug use. Several benefits of this method are anticipated. Alcohol and drug prevention researchers and practitioners will be able to use this approach to test the effectiveness of disseminated interventions and to quickly evaluate the potential of new and alternative prevention interventions. Specifically, this method will provide a means to evaluate the effectiveness of alcohol and drug prevention programs that are disseminated when randomization cannot occur. This method will also make evaluating new and adapted programs easier by reducing the challenges of recruitment, subject retention, and onsite data collection and pretest non-equivalence that often occur when l classrooms, schools and communities are assigned to condition. In this project, we will (1) gather data from previous longitudinal drug and alcohol research projects focused on adolescents; (2) harmonize these data to match a predefined coding scheme and include them in an integrated referential database; (3) refine and validate statistical models developed during Phase I to ensure algorithms used to generate virtual control cases are valid and replicable; (4) prepare a web-based system that will allow virtual control group technology to be maximally automated; and (5) conduct de novo field trials of disseminated interventions to demonstrate the viability of the system for providing meaningful comparisons.